Reward-modulated spike timing-dependent plasticity requires a reward-prediction system
نویسندگان
چکیده
منابع مشابه
Reward-modulated spike-timing-dependent plasticity with a dynamic spike timing rule and inhibitory plasticity
The viability of spike-timing-dependent plasticity (STDP) to explain learning processes is controversial, although recent developments of reward-modulated STDP (RM-STDP) models provide a plausible substrate. However, evidence has also emerged to show that rewards themselves can modify the STDP rule. In this modeling study, we use a dynamic STDP rule to show that such modification can lead to ne...
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Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how behaviorally relevant adaptive changes in complex networks of spiking neurons could be achieved in a self-organizing manner through local synaptic plasticity. However, the capabilities and limitations of this learning rule could so far only be tested through c...
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Spiking Neural Networks (SNNs) are one of the recent advances in machine learning that aim to further emulate the computations performed in the human brain. The efficiency of such networks stems from the fact that information is encoded as spikes, which is a paradigm shift from the computing model of the traditional neural networks. Spike Timing Dependent Plasticity (STDP), wherein the synaptic...
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Precise neuronal spike timing plays an important role in many aspects of cognitive processing. Here, we explore how a spiking neural network can learn to generate temporally precise spikes in response to a spatio-temporal pattern, through spike-timing-dependent plasticity modulated by a delayed reward signal. An escape noise neuron is implemented as the readout to incorporate the effect of back...
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2010
ISSN: 1662-453X
DOI: 10.3389/conf.fnins.2010.03.00221